We used Jensen-Shannon divergence [1]} to compare the curves corresponding to the performance of the estimated coverage method (Equation REF ) and our online evaluation metric \(\sigma \)  (Equation REF ) with the F-Score curve of EAL (see Fig. REF ). Our method \(\sigma \)  outperformed the estimated coverage method with a decrease of 96% in dissimilarity, which makes our method more reliable. Additionally, as shown in Table REF , on average \(\sigma \)  gives an accurate estimation of the F-Score without labeled data with an error margin of 3% on average when reaching 1.0 F-Score (i.e., @ 1.0 F).The \(\sigma \)  curves of GENIA-Cell-Type and GENIA-Virus shows high overestimation of the F-Score curve in the first iterations. This is due to missing entity annotations we found in the gold standard, which mistakenly shows that we had many false positives. To list a few, GENIA-Cell-Type have missing annotations for “transformed T cells”, “transformed cells”, and “T cells”, where GENIA-Virus have some missing annotations for “HIV-1” and “HIV-2”.
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